Overview

Dataset statistics

Number of variables17
Number of observations206068
Missing cells6291
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory143.1 MiB
Average record size in memory728.0 B

Variable types

CAT10
NUM7

Warnings

MUNICIPIO has a high cardinality: 1018 distinct values High cardinality
SUBGRUPO DE CULTIVO has a high cardinality: 120 distinct values High cardinality
CULTIVO has a high cardinality: 223 distinct values High cardinality
DESAGREGACIÓN REGIONAL Y/O SISTEMA PRODUCTIVO has a high cardinality: 271 distinct values High cardinality
NOMBRE CIENTIFICO has a high cardinality: 214 distinct values High cardinality
CÓD. MUN. is highly correlated with CÓD. DEP.High correlation
CÓD. DEP. is highly correlated with CÓD. MUN.High correlation
Área Cosechada (ha) is highly correlated with Área Sembrada (ha)High correlation
Área Sembrada (ha) is highly correlated with Área Cosechada (ha)High correlation
Rendimiento (t/ha) has 3433 (1.7%) missing values Missing
NOMBRE CIENTIFICO has 2857 (1.4%) missing values Missing
Producción (t) is highly skewed (γ1 = 57.01210037) Skewed
Área Cosechada (ha) has 4362 (2.1%) zeros Zeros
Producción (t) has 3807 (1.8%) zeros Zeros

Reproduction

Analysis started2020-12-12 23:20:15.295578
Analysis finished2020-12-12 23:20:30.694329
Duration15.4 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CÓD. DEP.
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.32256343
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2020-12-12T18:20:30.753380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q117
median41
Q368
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)51

Descriptive statistics

Standard deviation25.27866166
Coefficient of variation (CV)0.6269110769
Kurtosis-1.279010676
Mean40.32256343
Median Absolute Deviation (MAD)24
Skewness0.2304137871
Sum8309190
Variance639.0107354
MonotocityNot monotonic
2020-12-12T18:20:30.831948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
152057610.0%
 
5187599.1%
 
25178058.6%
 
41159267.7%
 
76157747.7%
 
68146727.1%
 
52134456.5%
 
5497514.7%
 
7385954.2%
 
1983854.1%
 
1753422.6%
 
5052412.5%
 
2351212.5%
 
1350682.5%
 
2048872.4%
 
7045032.2%
 
2740762.0%
 
6339131.9%
 
4739081.9%
 
838091.8%
 
4432861.6%
 
8531421.5%
 
6628101.4%
 
1823441.1%
 
8617760.9%
 
Other values (7)31541.5%
 
ValueCountFrequency (%) 
5187599.1%
 
838091.8%
 
1350682.5%
 
152057610.0%
 
1753422.6%
 
1823441.1%
 
1983854.1%
 
2048872.4%
 
2351212.5%
 
25178058.6%
 
ValueCountFrequency (%) 
996680.3%
 
972750.1%
 
954870.2%
 
941620.1%
 
915670.3%
 
881380.1%
 
8617760.9%
 
8531421.5%
 
818570.4%
 
76157747.7%
 

DEPARTAMENTO
Categorical

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
BOYACA
20576 
ANTIOQUIA
18759 
CUNDINAMARCA
17805 
HUILA
15926 
VALLE DEL CAUCA
15774 
Other values (27)
117228 
ValueCountFrequency (%) 
BOYACA2057610.0%
 
ANTIOQUIA187599.1%
 
CUNDINAMARCA178058.6%
 
HUILA159267.7%
 
VALLE DEL CAUCA157747.7%
 
SANTANDER146727.1%
 
NARIÑO134456.5%
 
NORTE DE SANTANDER97514.7%
 
TOLIMA85954.2%
 
CAUCA83854.1%
 
CALDAS53422.6%
 
META52412.5%
 
CORDOBA51212.5%
 
BOLIVAR50682.5%
 
CESAR48872.4%
 
SUCRE45032.2%
 
CHOCO40762.0%
 
QUINDIO39131.9%
 
MAGDALENA39081.9%
 
ATLANTICO38091.8%
 
LA GUAJIRA32861.6%
 
CASANARE31421.5%
 
RISARALDA28101.4%
 
CAQUETA23441.1%
 
PUTUMAYO17760.9%
 
Other values (7)31541.5%
 
2020-12-12T18:20:30.917021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:20:30.999092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length7
Mean length8.398582021
Min length4

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A37510521.7%
 
C1434678.3%
 
N1423268.2%
 
I1178436.8%
 
O1047916.1%
 
E1005365.8%
 
R986715.7%
 
L960665.6%
 
U960285.5%
 
D897915.2%
 
T785074.5%
 
547503.2%
 
S462252.7%
 
M378922.2%
 
B307651.8%
 
Q250161.4%
 
Y224901.3%
 
V224101.3%
 
H206701.2%
 
Ñ134450.8%
 
G78430.5%
 
J32860.2%
 
P21890.1%
 
Z567< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter167592996.8%
 
Space Separator547503.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A37510522.4%
 
C1434678.6%
 
N1423268.5%
 
I1178437.0%
 
O1047916.3%
 
E1005366.0%
 
R986715.9%
 
L960665.7%
 
U960285.7%
 
D897915.4%
 
T785074.7%
 
S462252.8%
 
M378922.3%
 
B307651.8%
 
Q250161.5%
 
Y224901.3%
 
V224101.3%
 
H206701.2%
 
Ñ134450.8%
 
G78430.5%
 
J32860.2%
 
P21890.1%
 
Z567< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
54750100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin167592996.8%
 
Common547503.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A37510522.4%
 
C1434678.6%
 
N1423268.5%
 
I1178437.0%
 
O1047916.3%
 
E1005366.0%
 
R986715.9%
 
L960665.7%
 
U960285.7%
 
D897915.4%
 
T785074.7%
 
S462252.8%
 
M378922.3%
 
B307651.8%
 
Q250161.5%
 
Y224901.3%
 
V224101.3%
 
H206701.2%
 
Ñ134450.8%
 
G78430.5%
 
J32860.2%
 
P21890.1%
 
Z567< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
54750100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII171723499.2%
 
None134450.8%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A37510521.8%
 
C1434678.4%
 
N1423268.3%
 
I1178436.9%
 
O1047916.1%
 
E1005365.9%
 
R986715.7%
 
L960665.6%
 
U960285.6%
 
D897915.2%
 
T785074.6%
 
547503.2%
 
S462252.7%
 
M378922.2%
 
B307651.8%
 
Q250161.5%
 
Y224901.3%
 
V224101.3%
 
H206701.2%
 
G78430.5%
 
J32860.2%
 
P21890.1%
 
Z567< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
Ñ13445100.0%
 

CÓD. MUN.
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1105
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40747.53407
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2020-12-12T18:20:31.079161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5475
Q117524
median41357
Q368001
95-th percentile76845
Maximum99773
Range94772
Interquartile range (IQR)50477

Descriptive statistics

Standard deviation25256.62191
Coefficient of variation (CV)0.6198319109
Kurtosis-1.278390027
Mean40747.53407
Median Absolute Deviation (MAD)24344
Skewness0.2304574563
Sum8396762850
Variance637896950.1
MonotocityNot monotonic
2020-12-12T18:20:31.165735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
761116550.3%
 
412986390.3%
 
768926340.3%
 
768346270.3%
 
413066230.3%
 
410016020.3%
 
766226020.3%
 
50015950.3%
 
413965910.3%
 
410205900.3%
 
761005850.3%
 
416155820.3%
 
660015260.3%
 
765205180.3%
 
411325130.2%
 
545185060.2%
 
410785020.2%
 
415484900.2%
 
415514730.2%
 
762754720.2%
 
418074700.2%
 
763644690.2%
 
54404650.2%
 
413194620.2%
 
760014590.2%
 
Other values (1080)19241893.4%
 
ValueCountFrequency (%) 
50015950.3%
 
50021740.1%
 
500491< 0.1%
 
50211170.1%
 
50301390.1%
 
50311220.1%
 
50342090.1%
 
50361220.1%
 
50381340.1%
 
50401170.1%
 
ValueCountFrequency (%) 
997732530.1%
 
996241230.1%
 
995241560.1%
 
990011360.1%
 
9766659< 0.1%
 
971611080.1%
 
970011080.1%
 
952001250.1%
 
950251170.1%
 
950151100.1%
 

MUNICIPIO
Categorical

HIGH CARDINALITY

Distinct1018
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size1.6 MiB
BOLIVAR
 
1012
LA UNION
 
916
SAN PEDRO
 
915
BUENAVISTA
 
859
GUADALUPE
 
727
Other values (1013)
201638 
ValueCountFrequency (%) 
BOLIVAR10120.5%
 
LA UNION9160.4%
 
SAN PEDRO9150.4%
 
BUENAVISTA8590.4%
 
GUADALUPE7270.4%
 
VILLANUEVA7210.3%
 
GRANADA6780.3%
 
BUGA6550.3%
 
SANTA BARBARA6460.3%
 
GARZON6390.3%
 
YUMBO6340.3%
 
TULUA6270.3%
 
GIGANTE6230.3%
 
CORDOBA6050.3%
 
ROLDANILLO6020.3%
 
NEIVA6020.3%
 
MEDELLIN5950.3%
 
RESTREPO5920.3%
 
LA PLATA5910.3%
 
ALGECIRAS5900.3%
 
RIVERA5820.3%
 
PALESTINA5600.3%
 
SANTUARIO5340.3%
 
SUCRE5270.3%
 
PEREIRA5260.3%
 
Other values (993)18950992.0%
 
2020-12-12T18:20:31.261317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T18:20:31.344889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length8
Mean length8.507575169
Min length3

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A32761218.7%
 
O1348877.7%
 
E1279327.3%
 
I1271987.3%
 
R1177616.7%
 
N1084206.2%
 
L1059686.0%
 
S819824.7%
 
C815874.7%
 
T771854.4%
 
U728044.2%
 
718934.1%
 
M480272.7%
 
D465602.7%
 
P429242.4%
 
B423972.4%
 
G362832.1%
 
V261711.5%
 
H162040.9%
 
Z142280.8%
 
J131730.8%
 
Y97760.6%
 
Q95080.5%
 
F85260.5%
 
Ñ34010.2%
 
Other values (6)732< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter168081795.9%
 
Space Separator718934.1%
 
Open Punctuation213< 0.1%
 
Close Punctuation213< 0.1%
 
Lowercase Letter3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A32761219.5%
 
O1348878.0%
 
E1279327.6%
 
I1271987.6%
 
R1177617.0%
 
N1084206.5%
 
L1059686.3%
 
S819824.9%
 
C815874.9%
 
T771854.6%
 
U728044.3%
 
M480272.9%
 
D465602.8%
 
P429242.6%
 
B423972.5%
 
G362832.2%
 
V261711.6%
 
H162041.0%
 
Z142280.8%
 
J131730.8%
 
Y97760.6%
 
Q95080.6%
 
F85260.5%
 
Ñ34010.2%
 
X191< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
71893100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(213100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)213100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n266.7%
 
a133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin168082095.9%
 
Common723194.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A32761219.5%
 
O1348878.0%
 
E1279327.6%
 
I1271987.6%
 
R1177617.0%
 
N1084206.5%
 
L1059686.3%
 
S819824.9%
 
C815874.9%
 
T771854.6%
 
U728044.3%
 
M480272.9%
 
D465602.8%
 
P429242.6%
 
B423972.5%
 
G362832.2%
 
V261711.6%
 
H162041.0%
 
Z142280.8%
 
J131730.8%
 
Y97760.6%
 
Q95080.6%
 
F85260.5%
 
Ñ34010.2%
 
X191< 0.1%
 
Other values (3)115< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
7189399.4%
 
(2130.3%
 
)2130.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII174973899.8%
 
None34010.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A32761218.7%
 
O1348877.7%
 
E1279327.3%
 
I1271987.3%
 
R1177616.7%
 
N1084206.2%
 
L1059686.1%
 
S819824.7%
 
C815874.7%
 
T771854.4%
 
U728044.2%
 
718934.1%
 
M480272.7%
 
D465602.7%
 
P429242.5%
 
B423972.4%
 
G362832.1%
 
V261711.5%
 
H162040.9%
 
Z142280.8%
 
J131730.8%
 
Y97760.6%
 
Q95080.5%
 
F85260.5%
 
(213< 0.1%
 
Other values (5)519< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
Ñ3401100.0%
 

GRUPO DE CULTIVO
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
FRUTALES
50236 
CEREALES
36045 
HORTALIZAS
32032 
TUBERCULOS Y PLATANOS
30664 
LEGUMINOSAS
26368 
Other values (8)
30723 
ValueCountFrequency (%) 
FRUTALES5023624.4%
 
CEREALES3604517.5%
 
HORTALIZAS3203215.5%
 
TUBERCULOS Y PLATANOS3066414.9%
 
LEGUMINOSAS2636812.8%
 
OTROS PERMANENTES2181310.6%
 
FIBRAS19771.0%
 
OLEAGINOSAS19671.0%
 
PLANTAS AROMATICAS, CONDIMENTARIAS Y MEDICINALES16860.8%
 
FORESTALES13270.6%
 
FLORES Y FOLLAJES9810.5%
 
OTROS TRANSITORIOS9560.5%
 
HONGOS16< 0.1%
 
2020-12-12T18:20:31.421455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:20:31.493016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length48
Median length10
Mean length12.02059029
Min length6

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S29615812.0%
 
E29248311.8%
 
A28251711.4%
 
L2156188.7%
 
R2031288.2%
 
T1964757.9%
 
O1778057.2%
 
U1379325.6%
 
N1103414.5%
 
928033.7%
 
I726862.9%
 
C717672.9%
 
F555022.2%
 
P541632.2%
 
M532392.1%
 
Y333311.3%
 
B326411.3%
 
H320481.3%
 
Z320321.3%
 
G283511.1%
 
D33720.1%
 
,16860.1%
 
J981< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter238257096.2%
 
Space Separator928033.7%
 
Other Punctuation16860.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S29615812.4%
 
E29248312.3%
 
A28251711.9%
 
L2156189.0%
 
R2031288.5%
 
T1964758.2%
 
O1778057.5%
 
U1379325.8%
 
N1103414.6%
 
I726863.1%
 
C717673.0%
 
F555022.3%
 
P541632.3%
 
M532392.2%
 
Y333311.4%
 
B326411.4%
 
H320481.3%
 
Z320321.3%
 
G283511.2%
 
D33720.1%
 
J981< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
92803100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,1686100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin238257096.2%
 
Common944893.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S29615812.4%
 
E29248312.3%
 
A28251711.9%
 
L2156189.0%
 
R2031288.5%
 
T1964758.2%
 
O1778057.5%
 
U1379325.8%
 
N1103414.6%
 
I726863.1%
 
C717673.0%
 
F555022.3%
 
P541632.3%
 
M532392.2%
 
Y333311.4%
 
B326411.4%
 
H320481.3%
 
Z320321.3%
 
G283511.2%
 
D33720.1%
 
J981< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
9280398.2%
 
,16861.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2477059100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S29615812.0%
 
E29248311.8%
 
A28251711.4%
 
L2156188.7%
 
R2031288.2%
 
T1964757.9%
 
O1778057.2%
 
U1379325.6%
 
N1103414.5%
 
928033.7%
 
I726862.9%
 
C717672.9%
 
F555022.2%
 
P541632.2%
 
M532392.1%
 
Y333311.3%
 
B326411.3%
 
H320481.3%
 
Z320321.3%
 
G283511.1%
 
D33720.1%
 
,16860.1%
 
J981< 0.1%
 

SUBGRUPO DE CULTIVO
Categorical

HIGH CARDINALITY

Distinct120
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
MAIZ
24965 
FRIJOL
14693 
TOMATE
 
9654
YUCA
 
9488
PLATANO
 
9048
Other values (115)
138220 
ValueCountFrequency (%) 
MAIZ2496512.1%
 
FRIJOL146937.1%
 
TOMATE96544.7%
 
YUCA94884.6%
 
PLATANO90484.4%
 
CAÑA80903.9%
 
CITRICOS77813.8%
 
PAPA74833.6%
 
ARROZ74163.6%
 
CAFE72633.5%
 
ARVEJA63603.1%
 
CACAO60662.9%
 
AGUACATE45422.2%
 
CEBOLLA40282.0%
 
FRUTALES EXOTICOS40151.9%
 
HABICHUELA35131.7%
 
MORA32131.6%
 
LULO30971.5%
 
AHUYAMA27921.4%
 
TOMATE DE ARBOL27271.3%
 
BANANO25651.2%
 
MANGO23151.1%
 
PATILLA23081.1%
 
PIÑA22141.1%
 
GUAYABA19881.0%
 
Other values (95)4844423.5%
 
2020-12-12T18:20:31.577089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-12T18:20:31.658659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length6
Mean length6.358823301
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A27980921.4%
 
O1169728.9%
 
I945227.2%
 
C935277.1%
 
R778465.9%
 
L759645.8%
 
E698675.3%
 
T698525.3%
 
M574354.4%
 
P426683.3%
 
U411953.1%
 
N388993.0%
 
Z365902.8%
 
F313812.4%
 
S292592.2%
 
J250401.9%
 
B205381.6%
 
H194071.5%
 
Y189221.4%
 
G173841.3%
 
170361.3%
 
Ñ118140.9%
 
D102110.8%
 
V90830.7%
 
X40150.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter129331498.7%
 
Space Separator170361.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A27980921.6%
 
O1169729.0%
 
I945227.3%
 
C935277.2%
 
R778466.0%
 
L759645.9%
 
E698675.4%
 
T698525.4%
 
M574354.4%
 
P426683.3%
 
U411953.2%
 
N388993.0%
 
Z365902.8%
 
F313812.4%
 
S292592.3%
 
J250401.9%
 
B205381.6%
 
H194071.5%
 
Y189221.5%
 
G173841.3%
 
Ñ118140.9%
 
D102110.8%
 
V90830.7%
 
X40150.3%
 
Q11140.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
17036100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin129331498.7%
 
Common170361.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A27980921.6%
 
O1169729.0%
 
I945227.3%
 
C935277.2%
 
R778466.0%
 
L759645.9%
 
E698675.4%
 
T698525.4%
 
M574354.4%
 
P426683.3%
 
U411953.2%
 
N388993.0%
 
Z365902.8%
 
F313812.4%
 
S292592.3%
 
J250401.9%
 
B205381.6%
 
H194071.5%
 
Y189221.5%
 
G173841.3%
 
Ñ118140.9%
 
D102110.8%
 
V90830.7%
 
X40150.3%
 
Q11140.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
17036100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII129853699.1%
 
None118140.9%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A27980921.5%
 
O1169729.0%
 
I945227.3%
 
C935277.2%
 
R778466.0%
 
L759645.8%
 
E698675.4%
 
T698525.4%
 
M574354.4%
 
P426683.3%
 
U411953.2%
 
N388993.0%
 
Z365902.8%
 
F313812.4%
 
S292592.3%
 
J250401.9%
 
B205381.6%
 
H194071.5%
 
Y189221.5%
 
G173841.3%
 
170361.3%
 
D102110.8%
 
V90830.7%
 
X40150.3%
 
Q11140.1%
 

Most frequent None characters

ValueCountFrequency (%) 
Ñ11814100.0%
 

CULTIVO
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
MAIZ
24965 
FRIJOL
14693 
TOMATE
 
9654
YUCA
 
9488
PLATANO
 
9048
Other values (218)
138220 
ValueCountFrequency (%) 
MAIZ2496512.1%
 
FRIJOL146937.1%
 
TOMATE96544.7%
 
YUCA94884.6%
 
PLATANO90484.4%
 
PAPA74833.6%
 
ARROZ74163.6%
 
CAFE72633.5%
 
CAÑA PANELERA66693.2%
 
ARVEJA63603.1%
 
CACAO60662.9%
 
AGUACATE45422.2%
 
HABICHUELA35131.7%
 
MORA32131.6%
 
LULO30971.5%
 
AHUYAMA27921.4%
 
TOMATE DE ARBOL27271.3%
 
CITRICOS26601.3%
 
MANGO23151.1%
 
NARANJA23091.1%
 
PATILLA23081.1%
 
CEBOLLA DE BULBO22951.1%
 
PIÑA22141.1%
 
BANANO21301.0%
 
GUAYABA19881.0%
 
Other values (198)5886028.6%
 
2020-12-12T18:20:31.747736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2020-12-12T18:20:31.830307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length6
Mean length6.655400159
Min length2

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A30865322.5%
 
O1149158.4%
 
R875906.4%
 
I835416.1%
 
L821686.0%
 
E809695.9%
 
C796835.8%
 
M644484.7%
 
T571274.2%
 
N541703.9%
 
P500703.7%
 
U440203.2%
 
Z385342.8%
 
313372.3%
 
B290032.1%
 
J278082.0%
 
F253591.8%
 
H237651.7%
 
Y197851.4%
 
G188721.4%
 
D145171.1%
 
S121300.9%
 
Ñ118880.9%
 
V99960.7%
 
Q11170.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter134012897.7%
 
Space Separator313372.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A30865323.0%
 
O1149158.6%
 
R875906.5%
 
I835416.2%
 
L821686.1%
 
E809696.0%
 
C796835.9%
 
M644484.8%
 
T571274.3%
 
N541704.0%
 
P500703.7%
 
U440203.3%
 
Z385342.9%
 
B290032.2%
 
J278082.1%
 
F253591.9%
 
H237651.8%
 
Y197851.5%
 
G188721.4%
 
D145171.1%
 
S121300.9%
 
Ñ118880.9%
 
V99960.7%
 
Q11170.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
31337100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin134012897.7%
 
Common313372.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A30865323.0%
 
O1149158.6%
 
R875906.5%
 
I835416.2%
 
L821686.1%
 
E809696.0%
 
C796835.9%
 
M644484.8%
 
T571274.3%
 
N541704.0%
 
P500703.7%
 
U440203.3%
 
Z385342.9%
 
B290032.2%
 
J278082.1%
 
F253591.9%
 
H237651.8%
 
Y197851.5%
 
G188721.4%
 
D145171.1%
 
S121300.9%
 
Ñ118880.9%
 
V99960.7%
 
Q11170.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
31337100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII135957799.1%
 
None118880.9%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A30865322.7%
 
O1149158.5%
 
R875906.4%
 
I835416.1%
 
L821686.0%
 
E809696.0%
 
C796835.9%
 
M644484.7%
 
T571274.2%
 
N541704.0%
 
P500703.7%
 
U440203.2%
 
Z385342.8%
 
313372.3%
 
B290032.1%
 
J278082.0%
 
F253591.9%
 
H237651.7%
 
Y197851.5%
 
G188721.4%
 
D145171.1%
 
S121300.9%
 
V99960.7%
 
Q11170.1%
 

Most frequent None characters

ValueCountFrequency (%) 
Ñ11888100.0%
 
Distinct271
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
MAIZ TRADICIONAL
20069 
FRIJOL
 
10776
YUCA
 
9284
PLATANO
 
8876
TOMATE
 
7598
Other values (266)
149465 
ValueCountFrequency (%) 
MAIZ TRADICIONAL200699.7%
 
FRIJOL107765.2%
 
YUCA92844.5%
 
PLATANO88764.3%
 
TOMATE75983.7%
 
CAFE72633.5%
 
CAÑA PANELERA66693.2%
 
ARVEJA63603.1%
 
CACAO60662.9%
 
PAPA58412.8%
 
MAIZ TECNIFICADO48962.4%
 
AGUACATE45422.2%
 
HABICHUELA35131.7%
 
MORA32131.6%
 
LULO30971.5%
 
ARROZ SECANO MANUAL29221.4%
 
AHUYAMA27921.4%
 
ARROZ RIEGO27461.3%
 
TOMATE DE ARBOL27271.3%
 
CITRICOS26601.3%
 
PATILLA23081.1%
 
CEBOLLA DE BULBO22951.1%
 
MANGO22351.1%
 
PIÑA22141.1%
 
TOMATE INVERNADERO20561.0%
 
Other values (246)7305035.4%
 
2020-12-12T18:20:31.921386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2020-12-12T18:20:32.008961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length7
Mean length8.994346526
Min length2

Overview of Unicode Properties

Unique unicode characters30
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A37730620.4%
 
O1571438.5%
 
I1460097.9%
 
R1195986.5%
 
C1189406.4%
 
L1130836.1%
 
E1030035.6%
 
N942805.1%
 
T851284.6%
 
790064.3%
 
M694173.7%
 
P514912.8%
 
U505672.7%
 
D446402.4%
 
Z404362.2%
 
B318981.7%
 
F302851.6%
 
J281191.5%
 
H245421.3%
 
G212411.1%
 
Y198501.1%
 
S187271.0%
 
V152820.8%
 
Ñ118880.6%
 
Q11170.1%
 
Other values (5)451< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter177430295.7%
 
Space Separator790064.3%
 
Open Punctuation63< 0.1%
 
Close Punctuation63< 0.1%
 
Dash Punctuation13< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A37730621.3%
 
O1571438.9%
 
I1460098.2%
 
R1195986.7%
 
C1189406.7%
 
L1130836.4%
 
E1030035.8%
 
N942805.3%
 
T851284.8%
 
M694173.9%
 
P514912.9%
 
U505672.8%
 
D446402.5%
 
Z404362.3%
 
B318981.8%
 
F302851.7%
 
J281191.6%
 
H245421.4%
 
G212411.2%
 
Y198501.1%
 
S187271.1%
 
V152820.9%
 
Ñ118880.7%
 
Q11170.1%
 
X276< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
79006100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(63100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)63100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin177430295.7%
 
Common791454.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A37730621.3%
 
O1571438.9%
 
I1460098.2%
 
R1195986.7%
 
C1189406.7%
 
L1130836.4%
 
E1030035.8%
 
N942805.3%
 
T851284.8%
 
M694173.9%
 
P514912.9%
 
U505672.8%
 
D446402.5%
 
Z404362.3%
 
B318981.8%
 
F302851.7%
 
J281191.6%
 
H245421.4%
 
G212411.2%
 
Y198501.1%
 
S187271.1%
 
V152820.9%
 
Ñ118880.7%
 
Q11170.1%
 
X276< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
7900699.8%
 
(630.1%
 
)630.1%
 
-13< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII184155999.4%
 
None118880.6%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A37730620.5%
 
O1571438.5%
 
I1460097.9%
 
R1195986.5%
 
C1189406.5%
 
L1130836.1%
 
E1030035.6%
 
N942805.1%
 
T851284.6%
 
790064.3%
 
M694173.8%
 
P514912.8%
 
U505672.7%
 
D446402.4%
 
Z404362.2%
 
B318981.7%
 
F302851.6%
 
J281191.5%
 
H245421.3%
 
G212411.2%
 
Y198501.1%
 
S187271.0%
 
V152820.8%
 
Q11170.1%
 
X276< 0.1%
 
Other values (4)175< 0.1%
 

Most frequent None characters

ValueCountFrequency (%) 
Ñ11888100.0%
 

AÑO
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.455976
Minimum2006
Maximum2018
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2020-12-12T18:20:32.072015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2007
Q12009
median2013
Q32015
95-th percentile2018
Maximum2018
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.479112868
Coefficient of variation (CV)0.001728789554
Kurtosis-1.173847126
Mean2012.455976
Median Absolute Deviation (MAD)3
Skewness-0.07451325979
Sum414702778
Variance12.10422635
MonotocityNot monotonic
2020-12-12T18:20:32.140574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
2017187569.1%
 
2016183998.9%
 
2015179008.7%
 
2013176498.6%
 
2014174348.5%
 
2012168568.2%
 
2010166198.1%
 
2011165928.1%
 
2009163187.9%
 
2008158947.7%
 
2007154837.5%
 
2018140966.8%
 
200640722.0%
 
ValueCountFrequency (%) 
200640722.0%
 
2007154837.5%
 
2008158947.7%
 
2009163187.9%
 
2010166198.1%
 
2011165928.1%
 
2012168568.2%
 
2013176498.6%
 
2014174348.5%
 
2015179008.7%
 
ValueCountFrequency (%) 
2018140966.8%
 
2017187569.1%
 
2016183998.9%
 
2015179008.7%
 
2014174348.5%
 
2013176498.6%
 
2012168568.2%
 
2011165928.1%
 
2010166198.1%
 
2009163187.9%
 

PERIODO
Categorical

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2018
 
8901
2017
 
8899
2016
 
8681
2015
 
8487
2014
 
8332
Other values (31)
162768 
ValueCountFrequency (%) 
201889014.3%
 
201788994.3%
 
201686814.2%
 
201584874.1%
 
201483324.0%
 
201382674.0%
 
201279623.9%
 
201177813.8%
 
201077363.8%
 
200976323.7%
 
200873513.6%
 
200770963.4%
 
2017A51962.5%
 
2018A51952.5%
 
2016A51632.5%
 
2013A50422.4%
 
2015A49712.4%
 
2014A48772.4%
 
2010A47962.3%
 
2011A47812.3%
 
2012A47112.3%
 
2017B46612.3%
 
2009A46022.2%
 
2016B45552.2%
 
2015B44422.2%
 
Other values (11)4595122.3%
 
2020-12-12T18:20:32.223645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:20:32.295707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.528675
Min length4

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
027445429.4%
 
222292423.9%
 
117089318.3%
 
A580036.2%
 
B509405.5%
 
7342393.7%
 
8299903.2%
 
6224712.4%
 
5179001.9%
 
3176491.9%
 
4174341.9%
 
9163181.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number82427288.3%
 
Uppercase Letter10894311.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
027445433.3%
 
222292427.0%
 
117089320.7%
 
7342394.2%
 
8299903.6%
 
6224712.7%
 
5179002.2%
 
3176492.1%
 
4174342.1%
 
9163182.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A5800353.2%
 
B5094046.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common82427288.3%
 
Latin10894311.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
027445433.3%
 
222292427.0%
 
117089320.7%
 
7342394.2%
 
8299903.6%
 
6224712.7%
 
5179002.2%
 
3176492.1%
 
4174342.1%
 
9163182.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A5800353.2%
 
B5094046.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII933215100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
027445429.4%
 
222292423.9%
 
117089318.3%
 
A580036.2%
 
B509405.5%
 
7342393.7%
 
8299903.2%
 
6224712.4%
 
5179001.9%
 
3176491.9%
 
4174341.9%
 
9163181.7%
 

Área Sembrada (ha)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5023
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.0738251
Minimum0
Maximum47403
Zeros721
Zeros (%)0.3%
Memory size1.6 MiB
2020-12-12T18:20:32.367269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median35
Q3151
95-th percentile1200
Maximum47403
Range47403
Interquartile range (IQR)141

Descriptive statistics

Standard deviation1153.602556
Coefficient of variation (CV)3.963264494
Kurtosis341.7829159
Mean291.0738251
Median Absolute Deviation (MAD)31
Skewness14.43974571
Sum59981001
Variance1330798.858
MonotocityNot monotonic
2020-12-12T18:20:32.451341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1081534.0%
 
577413.8%
 
270373.4%
 
363913.1%
 
2059542.9%
 
459112.9%
 
155832.7%
 
1551752.5%
 
648352.3%
 
3047562.3%
 
847002.3%
 
1236201.8%
 
735651.7%
 
5035641.7%
 
4034911.7%
 
2533201.6%
 
6025511.2%
 
10024911.2%
 
924771.2%
 
8023541.1%
 
3520811.0%
 
1820501.0%
 
7020021.0%
 
1117800.9%
 
15017420.8%
 
Other values (4998)10274449.9%
 
ValueCountFrequency (%) 
07210.3%
 
155832.7%
 
270373.4%
 
363913.1%
 
459112.9%
 
577413.8%
 
648352.3%
 
735651.7%
 
847002.3%
 
924771.2%
 
ValueCountFrequency (%) 
474031< 0.1%
 
465351< 0.1%
 
450001< 0.1%
 
447171< 0.1%
 
445502< 0.1%
 
436002< 0.1%
 
415442< 0.1%
 
388441< 0.1%
 
386781< 0.1%
 
384702< 0.1%
 

Área Cosechada (ha)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4557
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.4195217
Minimum0
Maximum38600
Zeros4362
Zeros (%)2.1%
Memory size1.6 MiB
2020-12-12T18:20:32.539917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median30
Q3130
95-th percentile1030
Maximum38600
Range38600
Interquartile range (IQR)122

Descriptive statistics

Standard deviation980.3752257
Coefficient of variation (CV)3.930627479
Kurtosis307.2537075
Mean249.4195217
Median Absolute Deviation (MAD)27
Skewness13.73481458
Sum51397382
Variance961135.5832
MonotocityNot monotonic
2020-12-12T18:20:32.626492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
281514.0%
 
581273.9%
 
1079303.8%
 
372233.5%
 
166493.2%
 
464823.1%
 
2055382.7%
 
849932.4%
 
1548992.4%
 
648712.4%
 
043622.1%
 
3043182.1%
 
739361.9%
 
1233361.6%
 
4032871.6%
 
2532221.6%
 
5031281.5%
 
929241.4%
 
1823551.1%
 
6023271.1%
 
10022461.1%
 
8021601.0%
 
3520731.0%
 
7019310.9%
 
1419240.9%
 
Other values (4532)9767647.4%
 
ValueCountFrequency (%) 
043622.1%
 
166493.2%
 
281514.0%
 
372233.5%
 
464823.1%
 
581273.9%
 
648712.4%
 
739361.9%
 
849932.4%
 
929241.4%
 
ValueCountFrequency (%) 
386001< 0.1%
 
351002< 0.1%
 
350001< 0.1%
 
343441< 0.1%
 
341752< 0.1%
 
337901< 0.1%
 
336002< 0.1%
 
335002< 0.1%
 
329321< 0.1%
 
329301< 0.1%
 

Producción (t)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10230
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2790.725595
Minimum0
Maximum4546116
Zeros3807
Zeros (%)1.8%
Memory size1.6 MiB
2020-12-12T18:20:32.722575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q132
median140
Q3648
95-th percentile5965.65
Maximum4546116
Range4546116
Interquartile range (IQR)616

Descriptive statistics

Standard deviation45114.71332
Coefficient of variation (CV)16.16594387
Kurtosis4247.754165
Mean2790.725595
Median Absolute Deviation (MAD)130
Skewness57.01210037
Sum575079242
Variance2035337358
MonotocityNot monotonic
2020-12-12T18:20:32.806647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
038071.8%
 
6025531.2%
 
2024971.2%
 
3024891.2%
 
824231.2%
 
524171.2%
 
1024011.2%
 
223871.2%
 
623451.1%
 
423341.1%
 
4022341.1%
 
1221531.0%
 
321091.0%
 
12021051.0%
 
1819671.0%
 
119120.9%
 
1519100.9%
 
2418360.9%
 
5017070.8%
 
15016720.8%
 
8016710.8%
 
9016240.8%
 
10016120.8%
 
1415420.7%
 
915100.7%
 
Other values (10205)15285174.2%
 
ValueCountFrequency (%) 
038071.8%
 
119120.9%
 
223871.2%
 
321091.0%
 
423341.1%
 
524171.2%
 
623451.1%
 
714050.7%
 
824231.2%
 
915100.7%
 
ValueCountFrequency (%) 
45461161< 0.1%
 
43004361< 0.1%
 
40853841< 0.1%
 
40107431< 0.1%
 
39994381< 0.1%
 
39923571< 0.1%
 
39169351< 0.1%
 
38999801< 0.1%
 
37234571< 0.1%
 
35970051< 0.1%
 

Rendimiento (t/ha)
Real number (ℝ≥0)

MISSING

Distinct3621
Distinct (%)1.8%
Missing3433
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean9.238819651
Minimum0.03
Maximum246
Zeros0
Zeros (%)0.0%
Memory size1.6 MiB
2020-12-12T18:20:32.894222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.6
Q11.5
median5
Q311.23
95-th percentile29
Maximum246
Range245.97
Interquartile range (IQR)9.73

Descriptive statistics

Standard deviation14.88865906
Coefficient of variation (CV)1.611532601
Kurtosis41.73865389
Mean9.238819651
Median Absolute Deviation (MAD)3.9
Skewness5.479609558
Sum1872108.22
Variance221.6721686
MonotocityNot monotonic
2020-12-12T18:20:32.977794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1094454.6%
 
876433.7%
 
174993.6%
 
674023.6%
 
573743.6%
 
1567623.3%
 
264333.1%
 
1261493.0%
 
460252.9%
 
758652.8%
 
1.556202.7%
 
2054042.6%
 
344242.1%
 
936821.8%
 
1.236081.8%
 
0.532991.6%
 
2.530711.5%
 
0.828771.4%
 
1828731.4%
 
0.625531.2%
 
2524081.2%
 
1623341.1%
 
1422041.1%
 
3021741.1%
 
0.720481.0%
 
Other values (3596)8345940.5%
 
(Missing)34331.7%
 
ValueCountFrequency (%) 
0.032< 0.1%
 
0.053< 0.1%
 
0.063< 0.1%
 
0.074< 0.1%
 
0.087< 0.1%
 
0.095< 0.1%
 
0.125< 0.1%
 
0.117< 0.1%
 
0.1210< 0.1%
 
0.138< 0.1%
 
ValueCountFrequency (%) 
2466< 0.1%
 
2402< 0.1%
 
2109< 0.1%
 
2061< 0.1%
 
200.691< 0.1%
 
2009< 0.1%
 
196.671< 0.1%
 
192.111< 0.1%
 
19018< 0.1%
 
187.53< 0.1%
 
Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
FRUTO FRESCO
59682 
GRANO SECO
57290 
HORTALIZA FRESCA
31742 
TUBERCULO FRESCO
21755 
PADDY VERDE
7416 
Other values (18)
28183 
ValueCountFrequency (%) 
FRUTO FRESCO5968229.0%
 
GRANO SECO5729027.8%
 
HORTALIZA FRESCA3174215.4%
 
TUBERCULO FRESCO2175510.6%
 
PADDY VERDE74163.6%
 
CAFE VERDE EQUIVALENTE72633.5%
 
PANELA66693.2%
 
LEGUMINOSA FRESCA39191.9%
 
HOJA FRESCA17990.9%
 
ACEITE CRUDO13820.7%
 
HOJA SECA13300.6%
 
LATEX SECO13230.6%
 
FIBRA SECA10230.5%
 
SEMILLA9540.5%
 
MELAZA7930.4%
 
CAÑA PARA MOLIENDA6280.3%
 
FLOR FRESCA5290.3%
 
FORRAJE FRESCO2340.1%
 
SEMILLA OLEAGINOSA1840.1%
 
FOLLAJE FRESCO1310.1%
 
HONGO FRESCO16< 0.1%
 
ND4< 0.1%
 
SEMILLA SECA2< 0.1%
 
2020-12-12T18:20:33.062868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:20:33.142936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length12
Mean length12.62710853
Min length2

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
O32125212.3%
 
R30898511.9%
 
E27872510.7%
 
A2137738.2%
 
C2131858.2%
 
2055397.9%
 
F1886697.3%
 
S1860187.1%
 
T1231474.7%
 
U1157564.4%
 
L773473.0%
 
N759732.9%
 
G614092.4%
 
I472811.8%
 
H348871.3%
 
Z325351.3%
 
D315251.2%
 
B227780.9%
 
V219420.8%
 
P147130.6%
 
Y74160.3%
 
Q72630.3%
 
M64800.2%
 
J34940.1%
 
X13230.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter239650492.1%
 
Space Separator2055397.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O32125213.4%
 
R30898512.9%
 
E27872511.6%
 
A2137738.9%
 
C2131858.9%
 
F1886697.9%
 
S1860187.8%
 
T1231475.1%
 
U1157564.8%
 
L773473.2%
 
N759733.2%
 
G614092.6%
 
I472812.0%
 
H348871.5%
 
Z325351.4%
 
D315251.3%
 
B227781.0%
 
V219420.9%
 
P147130.6%
 
Y74160.3%
 
Q72630.3%
 
M64800.3%
 
J34940.1%
 
X13230.1%
 
Ñ628< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
205539100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin239650492.1%
 
Common2055397.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O32125213.4%
 
R30898512.9%
 
E27872511.6%
 
A2137738.9%
 
C2131858.9%
 
F1886697.9%
 
S1860187.8%
 
T1231475.1%
 
U1157564.8%
 
L773473.2%
 
N759733.2%
 
G614092.6%
 
I472812.0%
 
H348871.5%
 
Z325351.4%
 
D315251.3%
 
B227781.0%
 
V219420.9%
 
P147130.6%
 
Y74160.3%
 
Q72630.3%
 
M64800.3%
 
J34940.1%
 
X13230.1%
 
Ñ628< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
205539100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2601415> 99.9%
 
None628< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
O32125212.3%
 
R30898511.9%
 
E27872510.7%
 
A2137738.2%
 
C2131858.2%
 
2055397.9%
 
F1886697.3%
 
S1860187.2%
 
T1231474.7%
 
U1157564.4%
 
L773473.0%
 
N759732.9%
 
G614092.4%
 
I472811.8%
 
H348871.3%
 
Z325351.3%
 
D315251.2%
 
B227780.9%
 
V219420.8%
 
P147130.6%
 
Y74160.3%
 
Q72630.3%
 
M64800.2%
 
J34940.1%
 
X13230.1%
 

Most frequent None characters

ValueCountFrequency (%) 
Ñ628100.0%
 

NOMBRE CIENTIFICO
Categorical

HIGH CARDINALITY
MISSING

Distinct214
Distinct (%)0.1%
Missing2857
Missing (%)1.4%
Memory size1.6 MiB
ZEA MAYS
25199 
PHASEOLUS VULGARIS
14693 
LYCOPERSICUM ESCULETUM
 
9654
MANIHOT ESCULENTA
 
9488
MUSA X PARADISIACA
 
9007
Other values (209)
135170 
ValueCountFrequency (%) 
ZEA MAYS2519912.2%
 
PHASEOLUS VULGARIS146937.1%
 
LYCOPERSICUM ESCULETUM96544.7%
 
MANIHOT ESCULENTA94884.6%
 
MUSA X PARADISIACA90074.4%
 
SACCHARUM OFFICINARUM80903.9%
 
SOLANUM TUBEROSUM74833.6%
 
ORYZA SATIVA74163.6%
 
COFFEA ARABICA72633.5%
 
PISUM SATIVUM63603.1%
 
THEOBROMA CACAO60662.9%
 
PERSEA AMERICANA MILL.45422.2%
 
JUDIAPHASEOLUS VULGARIS35131.7%
 
CAPSICUM ANNUM34801.7%
 
RUBUS GLAUCUS32131.6%
 
SOLANUM QUITOENSE30971.5%
 
CUCURBITA MOSHATA27921.4%
 
SOLAMUN BETACEUM27271.3%
 
CITRUS SPP.26601.3%
 
MANGIFERA INDICA23151.1%
 
CITRUS SINENSIS23091.1%
 
CITRULLUS VULGARIS23081.1%
 
ALLIUM CEPA22951.1%
 
ANANAS SATIVUS22141.1%
 
MUSA PARADISIACA21301.0%
 
Other values (189)5289725.7%
 
(Missing)28571.4%
 
2020-12-12T18:20:33.235016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2020-12-12T18:20:33.320590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length47
Median length16
Mean length15.62182872
Min length3

Overview of Unicode Properties

Unique unicode characters34
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A46687514.5%
 
S3006959.3%
 
U2540907.9%
 
I2469197.7%
 
2237647.0%
 
E1915395.9%
 
C1863455.8%
 
M1804005.6%
 
R1689555.2%
 
L1471534.6%
 
O1456484.5%
 
T1130353.5%
 
N1011323.1%
 
P877452.7%
 
H601941.9%
 
V533471.7%
 
Y484411.5%
 
F468831.5%
 
B408391.3%
 
G391561.2%
 
Z359011.1%
 
D307281.0%
 
.131320.4%
 
X119810.4%
 
J66200.2%
 
Other values (9)176420.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter297003692.3%
 
Space Separator2237647.0%
 
Other Punctuation138450.4%
 
Lowercase Letter85710.3%
 
Open Punctuation1465< 0.1%
 
Close Punctuation1465< 0.1%
 
Dash Punctuation13< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A46687515.7%
 
S30069510.1%
 
U2540908.6%
 
I2469198.3%
 
E1915396.4%
 
C1863456.3%
 
M1804006.1%
 
R1689555.7%
 
L1471535.0%
 
O1456484.9%
 
T1130353.8%
 
N1011323.4%
 
P877453.0%
 
H601942.0%
 
V533471.8%
 
Y484411.6%
 
F468831.6%
 
B408391.4%
 
G391561.3%
 
Z359011.2%
 
D307281.0%
 
X119810.4%
 
J66200.2%
 
Q35430.1%
 
W1033< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
223764100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1313294.9%
 
&7135.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n571466.7%
 
a285733.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-13100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1465100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1465100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin297860792.5%
 
Common2405527.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A46687515.7%
 
S30069510.1%
 
U2540908.5%
 
I2469198.3%
 
E1915396.4%
 
C1863456.3%
 
M1804006.1%
 
R1689555.7%
 
L1471534.9%
 
O1456484.9%
 
T1130353.8%
 
N1011323.4%
 
P877452.9%
 
H601942.0%
 
V533471.8%
 
Y484411.6%
 
F468831.6%
 
B408391.4%
 
G391561.3%
 
Z359011.2%
 
D307281.0%
 
X119810.4%
 
J66200.2%
 
n57140.2%
 
Q35430.1%
 
Other values (3)47290.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
22376493.0%
 
.131325.5%
 
(14650.6%
 
)14650.6%
 
&7130.3%
 
-13< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3219159100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A46687514.5%
 
S3006959.3%
 
U2540907.9%
 
I2469197.7%
 
2237647.0%
 
E1915395.9%
 
C1863455.8%
 
M1804005.6%
 
R1689555.2%
 
L1471534.6%
 
O1456484.5%
 
T1130353.5%
 
N1011323.1%
 
P877452.7%
 
H601941.9%
 
V533471.7%
 
Y484411.5%
 
F468831.5%
 
B408391.3%
 
G391561.2%
 
Z359011.1%
 
D307281.0%
 
.131320.4%
 
X119810.4%
 
J66200.2%
 
Other values (9)176420.5%
 

CICLO DE CULTIVO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
TRANSITORIO
108943 
PERMANENTE
82643 
ANUAL
14482 
ValueCountFrequency (%) 
TRANSITORIO10894352.9%
 
PERMANENTE8264340.1%
 
ANUAL144827.0%
 
2020-12-12T18:20:33.395654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:20:33.440192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:33.496741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.17728614
Min length5

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
T30052914.3%
 
R30052914.3%
 
N28871113.8%
 
E24792911.8%
 
A22055010.5%
 
I21788610.4%
 
O21788610.4%
 
S1089435.2%
 
P826433.9%
 
M826433.9%
 
U144820.7%
 
L144820.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2097213100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T30052914.3%
 
R30052914.3%
 
N28871113.8%
 
E24792911.8%
 
A22055010.5%
 
I21788610.4%
 
O21788610.4%
 
S1089435.2%
 
P826433.9%
 
M826433.9%
 
U144820.7%
 
L144820.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2097213100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T30052914.3%
 
R30052914.3%
 
N28871113.8%
 
E24792911.8%
 
A22055010.5%
 
I21788610.4%
 
O21788610.4%
 
S1089435.2%
 
P826433.9%
 
M826433.9%
 
U144820.7%
 
L144820.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2097213100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
T30052914.3%
 
R30052914.3%
 
N28871113.8%
 
E24792911.8%
 
A22055010.5%
 
I21788610.4%
 
O21788610.4%
 
S1089435.2%
 
P826433.9%
 
M826433.9%
 
U144820.7%
 
L144820.7%
 

Interactions

2020-12-12T18:20:22.476758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:22.607871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:22.734980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:22.867594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:22.998707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:23.137827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:23.269440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:23.401554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:23.533167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:23.661277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:23.793390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:23.924504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:24.063123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:24.193735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:24.325849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:24.460965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:24.594580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:24.731698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:24.868816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.010939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.148057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.282672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.412784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.540895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.672508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.802119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:25.937736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:26.070350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:26.200462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:26.340082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:26.478201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:26.620824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:26.760944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:26.909572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.052695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.192316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.324429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.454541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.587155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.717267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.853384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:27.984997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:28.118613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:28.251227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:28.382339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:28.516955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:28.649570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:28.786687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:28.922304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T18:20:33.561797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T18:20:33.677897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T18:20:33.795498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T18:20:33.920606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T18:20:34.053220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T18:20:29.405220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:29.783545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:30.248946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:20:30.405581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

CÓD. DEP.DEPARTAMENTOCÓD. MUN.MUNICIPIOGRUPO DE CULTIVOSUBGRUPO DE CULTIVOCULTIVODESAGREGACIÓN REGIONAL Y/O SISTEMA PRODUCTIVOAÑOPERIODOÁrea Sembrada (ha)Área Cosechada (ha)Producción (t)Rendimiento (t/ha)ESTADO FISICO PRODUCCIONNOMBRE CIENTIFICOCICLO DE CULTIVO
015BOYACA15114BUSBANZAHORTALIZASACELGAACELGAACELGA20062006B2111.00FRUTO FRESCOBETA VULGARISTRANSITORIO
125CUNDINAMARCA25754SOACHAHORTALIZASACELGAACELGAACELGA20062006B8280144018.00FRUTO FRESCOBETA VULGARISTRANSITORIO
225CUNDINAMARCA25214COTAHORTALIZASACELGAACELGAACELGA20062006B222617.33FRUTO FRESCOBETA VULGARISTRANSITORIO
354NORTE DE SANTANDER54405LOS PATIOSHORTALIZASACELGAACELGAACELGA20062006B334816.00FRUTO FRESCOBETA VULGARISTRANSITORIO
454NORTE DE SANTANDER54518PAMPLONAHORTALIZASACELGAACELGAACELGA20062006B11510.00FRUTO FRESCOBETA VULGARISTRANSITORIO
568SANTANDER68377LA BELLEZAHORTALIZASACELGAACELGAACELGA20062006B1166.00FRUTO FRESCOBETA VULGARISTRANSITORIO
625CUNDINAMARCA25754SOACHAHORTALIZASACELGAACELGAACELGA20072007A7270126018.00FRUTO FRESCOBETA VULGARISTRANSITORIO
725CUNDINAMARCA25214COTAHORTALIZASACELGAACELGAACELGA20072007A223417.00FRUTO FRESCOBETA VULGARISTRANSITORIO
854NORTE DE SANTANDER54518PAMPLONAHORTALIZASACELGAACELGAACELGA20072007A11510.00FRUTO FRESCOBETA VULGARISTRANSITORIO
968SANTANDER68377LA BELLEZAHORTALIZASACELGAACELGAACELGA20072007A1166.00FRUTO FRESCOBETA VULGARISTRANSITORIO

Last rows

CÓD. DEP.DEPARTAMENTOCÓD. MUN.MUNICIPIOGRUPO DE CULTIVOSUBGRUPO DE CULTIVOCULTIVODESAGREGACIÓN REGIONAL Y/O SISTEMA PRODUCTIVOAÑOPERIODOÁrea Sembrada (ha)Área Cosechada (ha)Producción (t)Rendimiento (t/ha)ESTADO FISICO PRODUCCIONNOMBRE CIENTIFICOCICLO DE CULTIVO
2060588ATLANTICO8520PALMAR DE VARELALEGUMINOSASFRIJOLFRIJOLZARAGOZA20152015A5482.00GRANO SECOPHASEOLUS VULGARISTRANSITORIO
2060598ATLANTICO8549PIOJOLEGUMINOSASFRIJOLFRIJOLZARAGOZA20152015A3100.30GRANO SECOPHASEOLUS VULGARISTRANSITORIO
20606025CUNDINAMARCA25436MANTAHORTALIZASCALABACINCALABACINZUCCHINI20172017A201939921.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606125CUNDINAMARCA25807TIBIRITAHORTALIZASCALABACINCALABACINZUCCHINI20172017A55408.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
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20606325CUNDINAMARCA25436MANTAHORTALIZASCALABACINCALABACINZUCCHINI20172017B201818010.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606425CUNDINAMARCA25524PANDIHORTALIZASCALABACINCALABACINZUCCHINI20172017B2285.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606525CUNDINAMARCA25436MANTAHORTALIZASCALABACINCALABACINZUCCHINI20182018A151515010.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606625CUNDINAMARCA25807TIBIRITAHORTALIZASCALABACINCALABACINZUCCHINI20182018A66508.27HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606725CUNDINAMARCA25524PANDIHORTALIZASCALABACINCALABACINZUCCHINI20182018A55255.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO